scholarly journals Identifying the Shortest Path in Large Networks using Boolean Satisfiability

2006 ◽  
Author(s):  
Fadi A. Aloul ◽  
Bashar Al Rawi ◽  
Mokhtar Aboelaze
Keyword(s):  
Shortest Path ◽  
Boolean Satisfiability ◽  
Large Networks

scholarly journals Negative Cost Girth Problem Using Map-Reduce Framework

2021 ◽  
Author(s):  
Mahboubeh Shamsi ◽  
Abdolreza Rasouli Kenari ◽  
Roghayeh Aghamohammadi
Keyword(s):  
Big Data ◽  
Shortest Path ◽  
Execution Time ◽  
Model Verification ◽  
Map Reduce ◽  
Polynomial Algorithms ◽  
Memory Consumption ◽  
Total Execution Time ◽  
Large Networks ◽  
Negative Cost Cycle

Abstract On a graph with a negative cost cycle, the shortest path is undefined, but the number of edges of the shortest negative cost cycle could be computed. It is called Negative Cost Girth (NCG). The NCG problem is applied in many optimization issues such as scheduling and model verification. The existing polynomial algorithms suffer from high computation and memory consumption. In this paper, a powerful Map-Reduce framework implemented to find the NCG of a graph. The proposed algorithm runs in O(log k) parallel time over O(n3) on each Hadoop nodes, where n; k are the size of the graph and the value of NCG, respectively. The Hadoop implementation of the algorithm shows that the total execution time is reduced by 50% compared with polynomial algorithms, especially in large networks concerning increasing the numbers of Hadoop nodes. The result proves the efficiency of the approach for solving the NCG problem to process big data in a parallel and distributed way.

Exact Distance Query in Large Graphs through Fast Graph Simplification

2020 ◽  
Author(s):  
Jun Liu ◽  
Yicheng Pan ◽  
Qifu Hu
Keyword(s):  
Graph Theory ◽  
Shortest Path ◽  
The Other ◽  
Large Networks ◽  
Large Graphs ◽  
Path Distance ◽  
Hub Network ◽  
Graph Simplification ◽  
Shortest Path Distance ◽  
Labeling Method

Abstract Shortest path distance query is one of the most fundamental problems in graph theory and applications. Nowadays, the scale of graphs becomes so large that traditional algorithms for shortest path are not available to answer the exact distance query quickly. Many methods based on two-hop labeling have been proposed to solve this problem. However, they cost too much either in preprocessing or query phase to handle large networks containing as many as tens of millions of vertices. In this paper, we propose a novel $k$-hub labeling method to address this problem in large networks with less preprocessing cost while keeping the query time in the microsecond level on average. Technically, two types of labels are presented in our construction, one for distance queries when the actual distance is at most $k-2$, which we call local label, and the other for further distance queries, which we call hub label. Our approach of $k$-hub labeling is essentially different from previous widely used two-hop labeling framework since we construct labels by using hub network structure. We conduct extensive experiments on large real-world networks and the results demonstrate the higher efficiency of our method in preprocessing phase and the much smaller space size of constructed index compared to previous efficient two-hop labeling method, with a comparatively fast query speed.

Shortest path analysis for efficient traversal queries in large networks

2014 ◽  
Vol 7 ◽  
pp. 811-816 ◽  
Author(s):  
Waqas Nawaz ◽  
Kifayat Ullah Khan ◽  
Young-Koo Lee
Keyword(s):  
Path Analysis ◽  
Shortest Path ◽  
Large Networks ◽  
Shortest Path Analysis

scholarly journals CENTRALITY ESTIMATION IN LARGE NETWORKS

2007 ◽  
Vol 17 (07) ◽  
pp. 2303-2318 ◽  
Author(s):  
ULRIK BRANDES ◽  
CHRISTIAN PICH
Keyword(s):  
Experimental Study ◽  
Network Analysis ◽  
Shortest Path ◽  
Shortest Paths ◽  
Single Source ◽  
Exact Computation ◽  
Large Networks ◽  
Selection Strategies

Centrality indices are an essential concept in network analysis. For those based on shortest-path distances the computation is at least quadratic in the number of nodes, since it usually involves solving the single-source shortest-paths (SSSP) problem from every node. Therefore, exact computation is infeasible for many large networks of interest today. Centrality scores can be estimated, however, from a limited number of SSSP computations. We present results from an experimental study of the quality of such estimates under various selection strategies for the source vertices.

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